Design Optimization of Concrete Gravity Dam Subjected to Near-field Earthquake Based on Novel Lean-bubble Sort Approach
Bibliographic record
Abstract
Due to limitations in traditional concrete gravity dam (CGD) design, a new approach is necessary. In this study, the lean analysis as a novel approach for CGD design, considering the interaction between dam and reservoir was considered. Maximum and minimum stresses at the heel and displacement of the crest were obtained as crucial input values of bubble sorting based on seismic analysis using Finite element analysis (FEA), and the Fuzzy Analytic Hierarchy Process (FAHP). The fuzzy bubble sorting analytic process, aimed at developing a novel method for selecting the best CGD configuration, was developed. Required Criteria, Sub-Criteria and developed models were applied to optimize the body of CGD. The weight of each sub-criterion and models were calculated based on pairwise comparison matrices. The novel approach was designed in MATLAB with the OPT-CGD code to select the best CGD model. The best weight of the Criteria, for selecting the best CGD model, based on the lean construction principles was selected from 60 developed models under implicit dynamic analysis. Statistical analysis reveals a 20% reduction in the concrete mass of the case study's optimal body compared to the traditionally designed dam.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".